Dr. Etienne Mueller is an Assistant Professor at CMKL University, specializing in computational neuroscience, neuromorphic computing, and biologically inspired artificial intelligence. His research focuses on developing brain-inspired neural network models that combine insights from neuroscience with machine learning to create more efficient, scalable, and energy-efficient AI systems.

Before joining CMKL University, Dr. Mueller was a Postdoctoral Researcher at the University of Melbourne, where he developed neural network growth algorithms from brain imaging data. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) in collaboration with Infineon Technologies AG.

Dr. Mueller has extensive experience in both academia and industry, having worked with organizations including Infineon Technologies, BMW Group, and Flowers Software. He has taught courses in cognitive systems, supervised graduate research in deep learning and spiking neural networks, and authored numerous publications in leading international AI and computational neuroscience conferences and journals.

• Ph.D., Computer Science, Technical University of Munich, Germany
• M.S., Product Development, Materials and Production, Technical University of Hamburg, Germany
• B.S., Mechanical Engineering, Technical University of Hamburg, Germany

Computational neuroscience, neuromorphic computing, spiking neural networks, biologically inspired artificial intelligence, deep learning, brain-inspired computing, energy-efficient AI, neural connectivity modeling, and intelligent autonomous systems.

1. D. Auge, J. Hille, E. Mueller, and A. Knoll, "A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks," Neural Processing Letters, vol. 53, no. 6, pp. 4693–4710, 2021.
2. E. Mueller, V. Studenyak, D. Auge, and A. Knoll, "Spiking Transformer Networks: A Rate Coded Approach for Processing Sequential Data," in Proc. ICSAI, 2021, pp. 1–5.
3. E. Mueller, D. Auge, S. Klimaschka, and A. Knoll, "Neural oscillations for energy-efficient hardware implementation of sparsely activated deep spiking neural networks," in Proc. AAAI, 2022.
4. E. Mueller, D. Auge, and A. Knoll, "Exploiting inhomogeneities of subthreshold transistors as populations of spiking neurons," in Proc. ICNC-FSKD, Springer, 2022, pp. 483–492.
5. E. Mueller, J. Hansjakob, D. Auge, and A. Knoll, "Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks," in Proc. IJCNN, 2021, pp. 1–8.